A Novel Connectionist System for Unconstrained Handwriting Recognition

By Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, J. Urgen Schmidhuber. 2009

1,960 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation (2016, methodology): “LSTM layer: In our experiments, instead of an RNN, a specific type of recurrent layer called LSTM (Hochreiter and Schmidhuber, 1997; Graves et al., 2009) is used.”
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models (2014, methodology): “LSTMs have been used to achieve state-of-the-art performance in several tasks such as handwriting recognition [31], sequence generation [32] speech recognition [33] and machine translation [11] among others.”
On the difficulty of training recurrent neural networks (2012, background): “In Hochreiter and Schmidhuber (1997); Graves et al. (2009) a solution is proposed for the vanishing gradients problem, where the structure of the model is changed.”
Recurrent Neural Network Regularization (2014, background): “There have been a number of architectural variants of the RNN that perform better on problems with long term dependencies (Hochreiter & Schmidhuber, 1997; Graves et al., 2009; Cho et al., 2014; Jaeger et al., 2007; Koutn´ık et al., 2014; Sundermeyer et al., 2012).”
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems (2015, background): “It has been shown to be effective in a variety of tasks, such as speech recognition (Graves et al., 2013b), handwriting recognition (Graves et al., 2009), spoken language understanding (Yao et al., 2014), and machine translation (Sutskever et al., 2014).”
How to Construct Deep Recurrent Neural Networks (2013, background): “…for various task such as language modeling (see, e.g., Graves, 2013; Pascanu et al. , 2013a; Mikolov, 2012; Sutskever et al. , 2011), learning word embeddings (see, e.g., Mikolov et al. , 2013a), online handwritten recognition (Graves et al. , 2009) and speech recognition (Graves et al. , 2013).”
LSTM: A Search Space Odyssey (2015, methodology): “This includes handwriting recognition (Graves et al., 2009; Pham et al., 2013; Doetsch et al., 2014) and generation (Graves et al., 2013), language modeling (Zaremba et al., 2014) and translation (Luong et al., 2014), acoustic modeling of speech (Sak et al., 2014), speech synthesis (Fan et al.,…”
Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism (2016, background): “Some of the earlier successes in this task have, for instance, been handwriting recognition (Bottou et al., 1997; Graves et al., 2009) and speech recognition (Graves et al., 2006; Chorowski et al., 2015).”
Beyond short snippets: Deep networks for video classification (2015, background): “For this reason, LSTMs yield state-of-the-art results in handwriting recognition [8, 10], speech recognition [9, 7], phoneme detection [5], emotion detection [25], segmentation of meetings and events [18], and evaluating programs [27].”
A Clockwork RNN (2014, background): “These networks have been very successful recently in speech and handwriting recognition (Graves et al., 2005; 2009; Sak et al., 2014).”
Dropout Improves Recurrent Neural Networks for Handwriting Recognition (2013, neutral): “[32] - - 25.”
Deep learning in neural networks: An overview (2014, background): “…analysis (Hochreiter & Obermayer, 2005), handwriting recognition (Bluche et al., 2014; Graves, Fernandez, Liwicki, Bunke, & Schmidhuber, 2008; Graves et al., 2009; Graves & Schmidhuber, 2009), voice activity detection (Eyben, Weninger, Squartini, & Schuller, 2013), optical character…”
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, methodology): “…Eyben 2009; Graves et al. 2013; Sak et al. 2014], time-series prediction [Hochreiter and Schmidhuber, 1997; Gers, 2000; Yim, 2002; Zhang, 2005; Graves et al., 2009, 2013; Schmidhuber, 2015; Brownlee, 2016; Gamboa, 2017; Roondiwala, 2017] as well as its applications in economics and finance…”
Learning to Diagnose with LSTM Recurrent Neural Networks (2015, background): “…spanning natural language processing, image captioning, handwriting recognition, and genomic analysis (Auli et al., 2013; Sutskever et al., 2014; Vinyals et al., 2015; Karpathy & Fei-Fei, 2015; Liwicki et al., 2007; Graves et al., 2009; Pollastri et al., 2002; Vohradsk´y, 2001; Xu et al., 2007).”
End-to-end learning of semantic role labeling using recurrent neural networks (2015, background): “(Graves et al., 2009) The effect of the gates is to allow the cells to store and access information over long periods of time.”
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015, methodology): “Although LSTM has drawn much attention from many researchers, GRU has recently shown to have similar performance as LSTM, while employing a simpler architecture [9].”